BME 580.689 Computational Personal Genomics. This Course Is Cross-Listed in Computer Science Where It Counts As a Regular CS Course

BME 580.689 Computational Personal Genomics. This course is cross-listed in Computer Science where it counts as a regular CS course. Professor: Steven Salzberg, http://ccb.jhu.edu/people/salzberg Times: Tuesdays and Thursdays, 9:00am – 10:15am Location: Malone Hall, room 228 [exception: meeting on Feb 19 in Malone 107] Textbook: None, but readings will be required throughout the semester and announced in class or on this syllabus. GRADING POLICY: Four laboratory assignments are worth 20% each. The in-class presentation is worth 10%, and the final exam is worth 10%. SYLLABUS (Note: this is a new course and this schedule will change during the semester; refresh your browser for updates. Readings will be posted here or announced in class.) Week 1: 27-29 January Tuesday: Introduction to the course. Biology and genomics background, genome sequencing technology. Course logistics. Thursday: Motivating example: the discovery of the Huntington’s Disease gene. Review of pairwise sequence alignment algorithms. Week 2: 3-5 February Tuesday: Sequencing technology, whole genome sequencing, and the first bacterial genome. Thursday: The human genome project, 1989-2003. The F1000 review site. Week 3: 10-12 February Tuesday: Alignment of whole genomes, alignment of a short query against a genome. Thursday: Methods for SNP discovery, from anthrax to human. Week 4: 17-19 February [NOTE: meeting on Feb 19 in Malone Hall, room 107] Tuesday: Personal genomics: sequencing your genome. 23andMe: a case study Thursday: RNA-Seq analysis: reads to transcripts to quantification. Part 1, spliced alignment. Analysis of cancer genomes to find fusion genes. Week 5: 24-26 February Tuesday: Lab 1 due. RNA-Seq analysis continued: assembly and quantification with Cufflinks and StringTie. Thursday: Annotation: a brief history of gene finding and the human gene count. Introduction to computational gene finding. Week 6: 3-5 March Tuesday: The status of the human reference genome today: assembly, annotation, and single nucleotide polymorphisms (SNPs). The UCSC Genome Browser. Thursday: Data mining with IGV. Systematic sequencing errors and the problems they cause. RNA-DNA differences: RNA editing and the perils of large-scale genome data. Week 7: 10-12 March Tuesday: Using genome data to find relatedness between people. Session on ethics: gene patents and the BRCA1 and BRCA2 genes. Thursday: Lab 2 due. What makes people live longer? Sequencing the very old to find variants related to longevity. Spring break 16-20 March Week 8: 24-26 March Tuesday: In-depth analysis of a paper: M. Meyer et al., A High-Coverage Genome Sequence from an Archaic Denisovan Individual, Science 12 October 2012, pp 222-226. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3617501 Thursday: Exome analysis: exome sequencing to discover the causes of disease. The DIAMUND algorithm. Week 9: 31 March – 2 April Tuesday: Genome assembly: introduction to the problem and the main algorithms. Assembly of short reads and hybrid assembly of short and long reads. Thursday: Student presentations of selected papers. (10-15 minutes per presentation, 5 per class). Week 10: 7-9 April Tuesday: Lab 3 due. Student presentations of selected papers (10-15 minutes per presentation, 5 per class). Thursday: Student presentations of selected papers (10-15 minutes per presentation, 5 per class). Week 11: 14-16 April Tuesday: The human microbiome: introduction. Biological results: obesity-related microbes. Thursday: 16S sequencing data and methods for analysis. Week 12: 21-23 April Tuesday: Fast k-mer counting and the Kraken algorithm. Thursday: The 2014 Ebola story. Week 13: 28-30 April Tuesday: Lab 4 due. Topic TBD. Thursday: In-class final exam, open book/open laptop. .

Copy Link

Recommended publications

Attitudes Towards Personal Genomics Among Older Swiss Adults: an Exploratory Study
Research Collection Journal Article Attitudes towards personal genomics among older Swiss adults: An exploratory study Author(s): Mählmann, Laura; Röcke, Christina; Brand, Angela; Hafen, Ernst; Vayena, Effy Publication Date: 2016 Permanent Link: https://doi.org/10.3929/ethz-b-000114241 Originally published in: Applied and Translational Genomics 8, http://doi.org/10.1016/j.atg.2016.01.009 Rights / License: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International This page was generated automatically upon download from the ETH Zurich Research Collection. For more information please consult the Terms of use. ETH Library Applied & Translational Genomics 8 (2016) 9–15 Contents lists available at ScienceDirect Applied & Translational Genomics journal homepage: www.elsevier.com/locate/atg Attitudes towards personal genomics among older Swiss adults: An exploratory study Laura Mählmann a,b, Christina Röcke c, Angela Brand b, Ernst Hafen a,EffyVayenad,⁎ a Institute of Molecular Systems Biology, ETH Zurich, Auguste-Piccard-Hof 1, 8093 Zürich, Switzerland b Institute for Public Health Genomics, Faculty of Health, Medicine and Life Sciences, Maastricht University, PO Box 616, 6200 MD Maastricht, The Netherlands c University Research Priority Program “Dynamics of Healthy Aging”, University of Zurich, Andreasstrasse 15/Box 2, 8050 Zurich, Switzerland d Health Ethics and Policy Lab, Institute of Epidemiology, Biostatistics and Prevention, University of Zurich, Hirschengraben 84, 8001 Zurich, Switzerland article info abstract Objectives: To explore attitudes of Swiss older adults towards personal genomics (PG). Keywords: Personal genomics Methods: Using an anonymized voluntary paper-and-pencil survey, data were collected from 151 men and Attitudes of older adults women aged 60–89 years attending the Seniorenuniversität Zurich, Switzerland (Seniors' University).
[Show full text]
In Human-Computer Interaction Published, Sold and Distributed By: Now Publishers Inc
Full text available at: http://dx.doi.org/10.1561/1100000067 Communicating Personal Genomic Information to Non-experts: A New Frontier for Human-Computer Interaction Orit Shaer Wellesley College, Wellesley, MA, USA [email protected] Oded Nov New York University, USA [email protected] Lauren Westendorf Wellesley College, Wellesley, MA, USA [email protected] Madeleine Ball Open Humans Foundation, Boston, MA, USA [email protected] Boston — Delft Full text available at: http://dx.doi.org/10.1561/1100000067 Foundations and Trends R in Human-Computer Interaction Published, sold and distributed by: now Publishers Inc. PO Box 1024 Hanover, MA 02339 United States Tel. +1-781-985-4510 www.nowpublishers.com [email protected] Outside North America: now Publishers Inc. PO Box 179 2600 AD Delft The Netherlands Tel. +31-6-51115274 The preferred citation for this publication is O. Shaer, O. Nov, L. Wstendorf, and M. Ball. Communicating Personal Genomic Information to Non-experts: A New Frontier for Human-Computer Interaction. Foundations and Trends R in Human-Computer Interaction, vol. 11, no. 1, pp. 1–62, 2017. R This Foundations and Trends issue was typeset in LATEX using a class ﬁle designed by Neal Parikh. Printed on acid-free paper. ISBN: 978-1-68083-254-9 c 2017 O. Shaer, O. Nov, L. Wstendorf, and M. Ball All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, mechanical, photocopying, recording or otherwise, without prior written permission of the publishers. Photocopying. In the USA: This journal is registered at the Copyright Clearance Cen- ter, Inc., 222 Rosewood Drive, Danvers, MA 01923.
[Show full text]
Multigenic Condition Risk Assessment in Direct-To-Consumer Genomic Services Melanie Swan, MBA
ARTICLE Multigenic condition risk assessment in direct-to-consumer genomic services Melanie Swan, MBA Purpose: Gene carrier status and pharmacogenomic data may be de- November 2009, cover 127, 46, and 28 conditions for $429, $985, tectable from single nucleotide polymorphisms (SNPs), but SNP-based and $999, respectively. These 3 services allow consumers to down- research concerning multigenic common disease such as diabetes, can- load their raw genotyping data, which can then be reviewed in 1 cers, and cardiovascular disease is an emerging field. The many SNPs other genome browsers such as the wiki-based SNPedia, where and loci that may relate to common disease have not yet been compre- there are 35 publicly available whole and partial human genomes hensively identified and understood scientifically. In the interim, direct- as of November 2009. Pathway Genomics and Gene Essence have to-consumer (DTC) genomic companies have forged ahead in develop- launched more recently, in mid-2009, and cover 71 and 84 condi- ing composite risk interpretations for multigenic conditions. It is useful tions for $299 and $1,195 respectively. SeqWright also provides a to understand how variance in risk interpretation may arise. Methods: basic service, genotyping 1 million SNPs for $998. 23andMe, A comprehensive study was conducted to analyze the 213 conditions deCODEme, and Pathway Genomics provide 2 services, health covered by the 5 identifiable genome-wide DTC genomic companies, and ancestry testing. Not a lot is known about the overall DTC and the total SNPs (401) and loci (224) assessed in the 20 common genomic testing market size; however, 23andMe reported having 2 disease conditions with the greatest overlapping coverage.
[Show full text]
Personal Genomics for Bioinformaticians
Personal Genomics for Bioinformaticians Instructor: Melissa Gymrek Audience: UCSD grad students (Ph.D. and M.Sc. students) Objective: Given someone’s genome, what could you learn? (Students are encouraged to analyze their own 23&Me data). Student backgrounds: • Computer Science and Engineering • Electrical Engineering • Bioinformatics • Biomedical Sciences Modules: • Introduction to personal genomics • Ancestry • Complex traits • NGS • Mutation hunting Course Resources Course website: https://gymreklab.github.io/teaching/personal_genomics/personal_genomics_2017.html Syllabus: https://s3-us-west-2.amazonaws.com/cse291personalgenomics/genome_course_syllabus_winter17.pdf Materials for generating problem sets: https://github.com/gymreklab/personal-genomics-course-2017 Contact: [email protected] Bioinforma)cs/Genomics Courses @UCLA ¤ h"p://www.bioinformacs.ucla.edu/graduate-courses/ ¤ h"p://www.bioinformacs.ucla.edu/undergraduate-courses/ ¤ All courses require 1 year of programming, a probability course ¤ 3 courses are combined Graduate and Undergraduate Courses ¤ Undergraduate Courses enrollment are primarily students from Computer Science and Undergradaute Bioinformacs Minor (Mostly Life Science Students) ¤ Graduate Enrollment is mostly from Bioinformacs Ph.D. program, Gene0cs and Genomics Ph.D. program, CS Ph.D. Program. ¤ 3 courses are only Graduate Courses ¤ Enrollment is mostly Bioinformacs Ph.D. program and CS Ph.D. Program ¤ Courses are taught by faculty in Computer Science or Stas0cs (or have joint appointments there) Bioinforma)cs/Genomics Courses @UCLA ¤ CM221. Introduc0on to Bioinformacs ¤ Taught by Christopher Lee ¤ Focuses on Classical Bioinformacs Problems and Stas0cal Inference ¤ CM222. Algorithms in Bioinformacs ¤ Taught by Eleazar Eskin ¤ Focuses on Algorithms in Sequencing ¤ CM224. Computaonal Gene0cs ¤ Taught by Eran Halperin ¤ Focuses on Stas0cal Gene0cs and Machine Learning Methods ¤ CM225. Computaonal Methods in Genomics ¤ Taught by Jason Ernst and Bogdan Pasaniuc ¤ Has component of reading recent papers ¤ CM226.
[Show full text]
Personal Genomic Information Management and Personalized Medicine: Challenges, Current Solutions, and Roles of HIM Professionals
Personal Genomic Information Management and Personalized Medicine: Challenges, Current Solutions, and Roles of HIM Professionals Personal Genomic Information Management and Personalized Medicine: Challenges, Current Solutions, and Roles of HIM Professionals by Amal Alzu’bi, MS; Leming Zhou, PhD; and Valerie Watzlaf, PhD, RHIA, FAHIMA Abstract In recent years, the term personalized medicine has received more and more attention in the field of healthcare. The increasing use of this term is closely related to the astonishing advancement in DNA sequencing technologies and other high-throughput biotechnologies. A large amount of personal genomic data can be generated by these technologies in a short time. Consequently, the needs for managing, analyzing, and interpreting these personal genomic data to facilitate personalized care are escalated. In this article, we discuss the challenges for implementing genomics-based personalized medicine in healthcare, current solutions to these challenges, and the roles of health information management (HIM) professionals in genomics-based personalized medicine. Introduction Personalized medicine utilizes personal medical information to tailor strategies and medications for diagnosing and treating diseases in order to maintain people’s health.1 In this medical practice, physicians combine results from all available patient data (such as symptoms, traditional medical test results, medical history and family history, and certain personal genomic information) so that they can make accurate diagnoses and determine
[Show full text]
Genomics Education in the Era of Personal Genomics: Academic, Professional, and Public Considerations
International Journal of Molecular Sciences Review Genomics Education in the Era of Personal Genomics: Academic, Professional, and Public Considerations Kiara V. Whitley , Josie A. Tueller and K. Scott Weber * Department of Microbiology and Molecular Biology, 4007 Life Sciences Building, 701 East University Parkway, Brigham Young University, Provo, UT 84602, USA; [email protected] (K.V.W.); [email protected] (J.A.T.) * Correspondence: [email protected]; Tel.: +1-801-422-6259 Received: 31 December 2019; Accepted: 22 January 2020; Published: 24 January 2020 Abstract: Since the completion of the Human Genome Project in 2003, genomic sequencing has become a prominent tool used by diverse disciplines in modern science. In the past 20 years, the cost of genomic sequencing has decreased exponentially,making it affordable and accessible. Bioinformatic and biological studies have produced significant scientific breakthroughs using the wealth of genomic information now available. Alongside the scientific benefit of genomics, companies offer direct-to-consumer genetic testing which provide health, trait, and ancestry information to the public. A key area that must be addressed is education about what conclusions can be made from this genomic information and integrating genomic education with foundational genetic principles already taught in academic settings. The promise of personal genomics providing disease treatment is exciting, but many challenges remain to validate genomic predictions and diagnostic correlations. Ethical and societal concerns must also be addressed regarding how personal genomic information is used. This genomics revolution provides a powerful opportunity to educate students, clinicians, and the public on scientific and ethical issues in a personal way to increase learning.
[Show full text]
Personal Genomics Wynn K Meyer, Phd Bioscience in the 21St Century Lehigh University November 18, 2019 Draw Your Genome
Personal Genomics Wynn K Meyer, PhD Bioscience in the 21st Century Lehigh University November 18, 2019 Draw your genome. On your drawing, please label: • The region(s) that can help you figure out where your ancestors lived. • The region(s) that determine whether you will fall asleep in this class. • The region(s) that determine your eye color. • The region(s) that determine your skin color. • The region(s) that contribute to your risk of cancer. Guessing is fine! What does your drawing look like? Public domain photo / NASA] Courtesy: National Human Genome Research Institute [Public domain] If things were simple: Heart disease Eye color Skin color Cancer Stroke Diabetes Arthritis Courtesy: National Human Genome Research Institute [Public domain] Why not? How do we link phenotypes with the genetic variants that underlie them? Arthritis Heart disease Cancer Skin color Diabetes Stroke Eye color What are genetic variants? The term “allele” can refer either Zoom WAY in… to the nucleotide at the SNP Allele 1 (A/G or T/A) … …TTGACCCGTTATTG… or to the combination across both SNPs (AT/GA). …TTGGCCCGTTATAG… Allele 2 Either way, it represents one “version” of the sequence. Single Nucleotide Polymorphisms The combination of alleles that a (SNPs) person has at a SNP is called a genotype. What process generates SNPs? E.g., for SNP1: AA, AG, and GG. What do genetic variants do? Mostly nothing! From the 1000 Genomes Project Consortium (Nature 2015): • We find that a typical genome differs from Why? the reference human genome at 4.1 million to 5.0 million sites (out of 3.2 billion).
[Show full text]
Personal Genomes and Ethical Issues
Personal Genomes and Ethical Issues 02-223 How to Analyze Your Own Genome Declining Cost of Genome Sequencing • The genome sequencing is expected to happen rouAnely in the near future Schadt, MSB, 2012 The era of big data: the genome data are already being collected in a large scale and being mined for scienAfic discovery to drive more accurate descripAve and predicAve models that inform decision making for the best diagnosis and treatment choice for a given paent. Would you post your genome on the web? Genomes and Privacy • DNA sequence data contain informaon that can be used to uniquely idenAfy an individual (i.e., genome sequences are like fingerprints) • Balancing the need for scienAfic study and privacy Genomes and Privacy • Privacy concerns – Genome sequence data and other related types of data (gene expressions, clinical records, epigeneAc data, etc.) are collected for a large number of paents for medical research – Most types of data are freely available through internet except for genotype data • NCBI GEO database for gene expression data • The cancer genome atlas data portals – Genotype data are available to scienAsts through restricted access – ProtecAng parAcipants’ privacy through informed consent hWps://tcga-data.nci.nih.gov/tcga/tcgaHome2.jsp The Cancer Genome Atlas (TCGA) Data The Cancer Genome Atlas (TCGA) Data hWps://tcga-data.nci.nih.gov/tcga/tcgaCancerDetails.jsp? diseaseType=LAML&diseaseName=Acute%20Myeloid%20Leukemia Access Control for TCGA Data • Open access data Aer – De-idenAfied clinical and demographic data – Gene
[Show full text]
Meeting Report: “Metagenomics, Metadata and Meta-Analysis” (M3) Workshop at the Pacific Symposium on Biocomputing 2010
Standards in Genomic Sciences (2010) 2:357-360 DOI:10.4056/sigs.802738 Meeting Report: “Metagenomics, Metadata and Meta-analysis” (M3) Workshop at the Pacific Symposium on Biocomputing 2010 Lynette Hirschman1, Peter Sterk2,3, Dawn Field2, John Wooley4, Guy Cochrane5, Jack Gilbert6, Eugene Kolker7, Nikos Kyrpides8, Folker Meyer9, Ilene Mizrachi10, Yasukazu Nakamura11, Susanna-Assunta Sansone5, Lynn Schriml12, Tatiana Tatusova10, Owen White12 and Pelin Yilmaz13 1 Information Technology Center, The MITRE Corporation, Bedford, MA, USA 2 NERC Center for Ecology and Hydrology, Oxford, OX1 3SR, UK 3 Wellcome Trust Sanger Institute, Hinxton, Cambridge, UK 4 University of California San Diego, La Jolla, CA, USA 5 European Molecular Biology Laboratory (EMBL), European Bioinformatics Institute (EBI), Hinxton, Cambridge, UK 6 Plymouth Marine Laboratory (PML), Plymouth, UK 7 Seattle Children’s Hospital, Seattle, WA, USA 8 Genome Biology Program, DOE Joint Genome Institute, Walnut Creek, California, USA 9 Argonne National Laboratory, 9700 South Cass Avenue, Argonne, IL 60439, USA 10 National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA 11 DNA Data Bank of Japan, National Institute of Genetics, Yata, Mishima, Japan 12 University of Maryland School of Medicine, Baltimore, MD, USA 13 Microbial Genomics Group, Max Planck Institute for Marine Microbiology & Jacobs University Bremen, Bremen, Germany This report summarizes the M3 Workshop held at the January 2010 Pacific Symposium on Biocomputing. The workshop, organized by Genomic Standards Consortium members, in- cluded five contributed talks, a series of short presentations from stakeholders in the genom- ics standards community, a poster session, and, in the evening, an open discussion session to review current projects and examine future directions for the GSC and its stakeholders.
[Show full text]
The ELSI Advisory Board GP-Write/ the Center of Excellence For
! The ELSI Advisory Board GP-write/ The Center of Excellence for Engineering Biology Background Information on GP-write, with ELSI dimensions The Genome Project-write (GP-write) is an open, international research project led by a multi- disciplinary group of scientific leaders who will oversee a reduction in the costs of engineering and testing large genomes, including a human genome, in cell lines by over 1,000-fold within ten years. The overarching goal of such an effort is to further our understanding of the blueprint for life provided by the Human Genome Project (HGP-read) by developing new technologies and an ethical framework for genome-scale engineering, as well as transformative medical applications. GP-write will include whole genome engineering of human cell lines and other organisms of significance to research and development, agriculture, and public health, as well as organoids derived from human cells. A coordinated scientific effort to understand, discuss, and apply large genome editing technologies is timely, and public discourse regarding such an endeavor is both expected and encouraged as GP-write gets underway. However, responsible innovation requires having more than ELSI discussions; it also involves identifying common goals important to scientists and the public through timely and detailed consultation among diverse stakeholders. The GP-write effort is aimed at technology development and knowledge creation, with the ultimate goal of improving life on Earth. Potential innovations relate to biomedical therapies, food supply management, biofuels, and public health. However, as with many other technology development initiatives, there is potential for misuse, and some risks may be unforeseen.
[Show full text]
Seeing the Future? How Genetic Testing Will Impact Life Insurance
Seeing the future? How genetic testing will impact life insurance Table of contents Abstract 2 The evolution of genetic testing 3 Clinical genetic testing 5 The DTC genetic testing market 6 Insurance and genetic testing 8 Family history data protection 12 Health incentives for insurance clients 13 Increasing anti-selection exposure 14 Conclusion 15 Swiss Re Seeing the future? 1 Abstract The cost of sequencing an individual’s genome has fallen exponentially, so that genetics has started to become an integral part of clinical practice. This has provided physicians with a valuable diagnostic and in some cases a predictive pre-symptomatic tool, which they can use more effectively to manage patients’ diseases and even take preventive actions. The exponential fall in the price of genome analysis tools has also led to the robust growth of a direct-to-consumer market (DTC), allowing individuals to access quickly and cheaply their genetic profile. Insurers broadly welcome the increased clinical use of genetic services, as it can result in effective personalised treatments and preventive actions taken for patients. Insurers are, however, aware that there are some risks with the increased availability of genetic information. Most prominent of these is an information asymmetry. If individuals have access to genetic data that their insurer does not, it could be the basis of non-disclosure and anti-selection. Moreover, the effects of anti-selection could be amplified by different regulatory approaches to the use and disclosure of genetic tests for underwriting purposes. This paper frames the concerns of the insurance industry, and how important the right approach and incentives will be in ensuring the effective use of genetic data in health and insurance contexts.
[Show full text]
Personal Genomics Lecture Slides
Genomics, Bioinformatics & Medicine http://biochem158.stanford.edu/ Personal Genomics http://biochem158.stanford.edu/Personal%20Genomics.html The Lancet 2010, 375: 1525-1535. Doug Brutlag Professor Emeritus of Biochemistry & Medicine Stanford University School of Medicine © Doug Brutlag 2015 Genetic Penetrance of Inherited Diseases • Many inherited diseases are Mendelian and highly penetrant – Sickle cell disease – Thalassemias – Huntington’s disease – Color blindness – Cystic fbrosis • Most common diseases are complex (multifactorial - caused by multiple genes or multiple pathways as well as multiple environmental factors) and of low penetrance – Familial – Predisposition to disease – Very large environmental and/or behavioral component • Type I diabetes and other autoimmune diseases (lupus, rheumatoid arthritis, hyperthyroidism, Crohn’s disease, Celiac Sprue, irritable bowel disease etc.) • Type 2 diabetes • Coronary heart disease (atherosclerosis) • Asthma, COPD, pulmonary fbrosis – Many complex diseases can be avoided with diet, nutrition, exercise or behavioral modifcation – Many complex diseases can also be monitored by increased vigilance (another behavioral modifcation) © Doug Brutlag 2015 So What Can We Learn from Personal Genomics? • Disease risk for common diseases – Genetic predisposition towards a disease (relative risk or odds ratio) – Genetic versus environmental contributions to disease (penetrance) – How to alter your environment and behavior to avoid or detect the disease • Disease Carrier status – Premarital genetic
[Show full text]